It is well known that magnetic resonance imaging (MRI) can be used to display the density of nuclear spins, in particular of 1H, 31P and 23Na atoms, in the volume of an examination object as a function of the position. In the process, different tissue types are reproduced in the MR image with varying signal strength mainly based on the different spin-relaxation times in the tissues. The signal strengths acquired by the magnetic resonance imaging scanner during the scan which are associated with the respective voxels of the examination object depend on a number of parameters and are typically imaged as corresponding grayscale values in the image data. Hence, magnetic resonance imaging does not have standard values for the scan signal of certain tissue types nor a unit which can be compared to the Hounsfield Unit of computed tomography. Rather, the MR image data specifies fundamentally arbitrary units which cannot be directly evaluated diagnostically. Typically, the image is interpreted on the basis of the overall contrast, the respective weighting (e.g. T1, T2, T2* or PD weighting) on which the image data is based and the signal differences between different tissues.
Visualizing MR image data (2D or 3D MR image data) often requires the removal of anatomical structures imaged in the MR image data in order to end up with an unimpeded view of the anatomical objects of interest. Thus, in the case of examinations, treatments or interventions in the brain of a patient, the medical practitioner is interested in an exclusive representation or display of the brain which includes its structures, but is without bothersome cranial bones or other anatomical structures impeding the direct view of the brain.
These days, so-called direct volume rendering (DVR) for the 3D display of MR image data is routinely used in clinical practice. In the process, transfer functions image the measured value of the original data to colors and opacities in order to generate images which are as meaningful as possible. However, when there are spatially separate regions of an MR image data record with the same intensity value, transfer functions cannot offer a different display, and the structures toward the back are covered. An example of this are the previously mentioned MR image data records of the head, where as a result of equal measured values the brain is always covered by more outwardly lying tissue. Before corresponding MR image data is visualized/displayed/illustrated in such applications, the bothersome elements, e.g. cranial bones or objects not part of the anatomical structure of the brain, imaged in the MR image data are removed. In the prior art, methods for removing the cranial bones imaged in the MR image data are known as so-called skull stripping methods.
Since T1, T2, T2* or PD weighted MR image data in each case has different signal intensities for bone material and the brain, this has to be taken into account in the skull stripping methods. A further problem of known skull stripping methods lies in the fact that the MR image data often has anisotropic characteristics, i.e. image data values of one and the same imaged material for example can differ in various regions of an MR image. Moreover, the voxel geometries can vary in the MR image data. These are significant, but not the only, problems which have to be solved, at least to a great extent, by the best skull stripping methods of the prior art. Skull stripping methods therefore require complex image processing processes, and require high computational complexity and a correspondingly long calculation time.
These days, skull stripping methods are used within the scope of MR image data post-processing. They largely satisfy high requirements in respect of quality and accuracy. These methods are for example used to examine changes in the brain mass or brain volume or parts thereof. Here, the skull stripping method is part of a complex image data evaluation process which as a result supplies the desired numerical details regarding deviations of the brain mass or the brain volume. The algorithms used in this case are distinguished by great complexity. However, they are usually limited in their applicability to MR image data generated with certain recording parameters. Thus the algorithms cannot be used universally. In addition, the known skull stripping methods in part require that the brain is completely imaged in the MR image data and that the MR image data is distinguished by almost isotropic properties.
The skull stripping methods known in the prior art can mainly be subdivided into the following three categories: region-based methods, model-based methods and hybrid methods which comprise a combination of the abovementioned methods.
All known skull stripping methods use that MR image data as input data which should later be displayed, for example, without cranial bones or other bothersome elements. A mask is generated on the basis of this MR image data by means of a segmentation method, by means of which mask, for example, the cranial bones imaged in this MR image data can be hidden very accurately. Furthermore, these methods are so optimized and specific that they can in each case only be applied to MR image data recorded using specific parameters.
The known skull stripping methods typically require a few tens of seconds of calculation time before the MR image data processed by the skull stripping method can be displayed. These relatively long calculation times are often unacceptable in clinical operation. The treating medical practitioner often needs to have a quick overview over the cortex surface imaged in the MR image data. This includes, in particular, fast overview displays of image displays also composed of a number of MR image data (e.g. within the scope of functional magnetic resonance imaging (fMRI)).